Maurizio Garbarino

Learn More
We propose a framework to estimate player enjoyment preference from physiological signals. This can produce objective measures that could be used to adapt dynamically a game to maintain the player in an optimal status of enjoyment. We present a case study on The Open Racing Car Simulator (TORCS) video game. In particular, we focus both on the experimental(More)
—The Empatica E3 is a wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. The E3 has four embedded sensors: photoplethysmo-graph (PPG), electrodermal activity (EDA), 3-axis accelerometer, and temperature. It is small, light and comfortable and it is suitable for almost all real-life applications. The E3(More)
This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of(More)
In this paper we present a case study on The Open Racing Car Simulator (TORCS) video game with the aim of developing a classifier to recognize user enjoyment from physiological signals. Three classes of enjoyment, derived from pairwise comparison of different races, are considered for classification; impact of artifact reduction, normalization and feature(More)
  • 1